Overview

Dataset statistics

Number of variables19
Number of observations195
Missing cells674
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.1 KiB
Average record size in memory152.7 B

Variable types

Text2
Numeric15
Unsupported1
Categorical1

Alerts

Co2-Emissions per ton is highly overall correlated with GDP and 2 other fieldsHigh correlation
GDP is highly overall correlated with Co2-Emissions per ton and 3 other fieldsHigh correlation
Population is highly overall correlated with Co2-Emissions per ton and 1 other fieldsHigh correlation
Infant mortality is highly overall correlated with Minimum wage and 4 other fieldsHigh correlation
Minimum wage is highly overall correlated with Infant mortality and 3 other fieldsHigh correlation
temperature is highly overall correlated with Infant mortality and 3 other fieldsHigh correlation
Gini's index is highly overall correlated with Prevalence of moderate or severe food insecurity in the total population (percent) (2022)High correlation
GDP per capita is highly overall correlated with Infant mortality and 3 other fieldsHigh correlation
Human Development Index (2021) is highly overall correlated with GDP and 5 other fieldsHigh correlation
Prevalence of moderate or severe food insecurity in the total population (percent) (2022) is highly overall correlated with Co2-Emissions per ton and 7 other fieldsHigh correlation
ideal temperature? is highly overall correlated with temperatureHigh correlation
Country Code has 193 (99.0%) missing valuesMissing
Agricultural Land( %) has 7 (3.6%) missing valuesMissing
Co2-Emissions per ton has 7 (3.6%) missing valuesMissing
CPI has 17 (8.7%) missing valuesMissing
GDP has 2 (1.0%) missing valuesMissing
Infant mortality has 6 (3.1%) missing valuesMissing
Minimum wage has 45 (23.1%) missing valuesMissing
Unemployment rate has 19 (9.7%) missing valuesMissing
Population: Labor force participation (%) has 19 (9.7%) missing valuesMissing
temperature change per country in 2022 has 195 (100.0%) missing valuesMissing
temperature has 49 (25.1%) missing valuesMissing
Precipitation Depth (mm/year) has 15 (7.7%) missing valuesMissing
Gini's index has 45 (23.1%) missing valuesMissing
Human Development Index (2021) has 5 (2.6%) missing valuesMissing
Prevalence of moderate or severe food insecurity in the total population (percent) (2022) has 48 (24.6%) missing valuesMissing
Country has unique valuesUnique
temperature change per country in 2022 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-11-17 02:43:28.737177
Analysis finished2023-11-17 02:44:17.942668
Duration49.21 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct195
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:19.024623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.9076923
Min length4

Characters and Unicode

Total characters1737
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
republic 6
 
2.3%
and 6
 
2.3%
the 5
 
1.9%
of 4
 
1.6%
saint 3
 
1.2%
united 3
 
1.2%
guinea 3
 
1.2%
south 3
 
1.2%
korea 2
 
0.8%
sudan 2
 
0.8%
Other values (216) 221
85.7%
2023-11-17T10:44:20.386466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 262
15.1%
i 153
 
8.8%
n 135
 
7.8%
e 119
 
6.9%
r 94
 
5.4%
o 94
 
5.4%
t 75
 
4.3%
u 68
 
3.9%
63
 
3.6%
l 60
 
3.5%
Other values (42) 614
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1427
82.2%
Uppercase Letter 246
 
14.2%
Space Separator 63
 
3.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 262
18.4%
i 153
10.7%
n 135
9.5%
e 119
 
8.3%
r 94
 
6.6%
o 94
 
6.6%
t 75
 
5.3%
u 68
 
4.8%
l 60
 
4.2%
s 54
 
3.8%
Other values (16) 313
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 30
 
12.2%
M 20
 
8.1%
C 19
 
7.7%
B 19
 
7.7%
A 16
 
6.5%
T 15
 
6.1%
N 14
 
5.7%
G 14
 
5.7%
L 12
 
4.9%
I 11
 
4.5%
Other values (14) 76
30.9%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1673
96.3%
Common 64
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 262
15.7%
i 153
 
9.1%
n 135
 
8.1%
e 119
 
7.1%
r 94
 
5.6%
o 94
 
5.6%
t 75
 
4.5%
u 68
 
4.1%
l 60
 
3.6%
s 54
 
3.2%
Other values (40) 559
33.4%
Common
ValueCountFrequency (%)
63
98.4%
- 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 262
15.1%
i 153
 
8.8%
n 135
 
7.8%
e 119
 
6.9%
r 94
 
5.4%
o 94
 
5.4%
t 75
 
4.3%
u 68
 
3.9%
63
 
3.6%
l 60
 
3.5%
Other values (42) 614
35.3%

Country Code
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing193
Missing (%)99.0%
Memory size1.6 KiB
2023-11-17T10:44:20.755994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
ValueCountFrequency (%)
afg 1
50.0%
alb 1
50.0%
2023-11-17T10:44:21.252542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2
33.3%
F 1
16.7%
G 1
16.7%
L 1
16.7%
B 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
F 1
16.7%
G 1
16.7%
L 1
16.7%
B 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2
33.3%
F 1
16.7%
G 1
16.7%
L 1
16.7%
B 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2
33.3%
F 1
16.7%
G 1
16.7%
L 1
16.7%
B 1
16.7%

Agricultural Land( %)
Real number (ℝ)

MISSING 

Distinct168
Distinct (%)89.4%
Missing7
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean0.39117553
Minimum0.006
Maximum0.826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:21.523543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.006
5-th percentile0.04915
Q10.217
median0.396
Q30.55375
95-th percentile0.7485
Maximum0.826
Range0.82
Interquartile range (IQR)0.33675

Descriptive statistics

Standard deviation0.21783052
Coefficient of variation (CV)0.55686131
Kurtosis-0.94699995
Mean0.39117553
Median Absolute Deviation (MAD)0.176
Skewness0.090843607
Sum73.541
Variance0.047450135
MonotonicityNot monotonic
2023-11-17T10:44:21.785543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.174 3
 
1.5%
0.734 2
 
1.0%
0.233 2
 
1.0%
0.393 2
 
1.0%
0.345 2
 
1.0%
0.311 2
 
1.0%
0.715 2
 
1.0%
0.648 2
 
1.0%
0.231 2
 
1.0%
0.027 2
 
1.0%
Other values (158) 167
85.6%
(Missing) 7
 
3.6%
ValueCountFrequency (%)
0.006 1
0.5%
0.009 1
0.5%
0.014 1
0.5%
0.026 1
0.5%
0.027 2
1.0%
0.034 1
0.5%
0.038 1
0.5%
0.039 1
0.5%
0.046 1
0.5%
0.055 1
0.5%
ValueCountFrequency (%)
0.826 1
0.5%
0.808 1
0.5%
0.804 1
0.5%
0.798 1
0.5%
0.792 1
0.5%
0.777 1
0.5%
0.776 1
0.5%
0.764 1
0.5%
0.758 1
0.5%
0.752 1
0.5%

Co2-Emissions per ton
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct184
Distinct (%)97.9%
Missing7
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean177799.24
Minimum11
Maximum9893038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:22.045057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile187.7
Q12304.25
median12303
Q363884.25
95-th percentile556874.15
Maximum9893038
Range9893027
Interquartile range (IQR)61580

Descriptive statistics

Standard deviation838790.27
Coefficient of variation (CV)4.7176257
Kurtosis102.74825
Mean177799.24
Median Absolute Deviation (MAD)12072
Skewness9.5735134
Sum33426257
Variance7.0356911 × 1011
MonotonicityNot monotonic
2023-11-17T10:44:22.670139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 2
 
1.0%
495 2
 
1.0%
28284 2
 
1.0%
2017 2
 
1.0%
7407 1
 
0.5%
120369 1
 
0.5%
41023 1
 
0.5%
63457 1
 
0.5%
201150 1
 
0.5%
224 1
 
0.5%
Other values (174) 174
89.2%
(Missing) 7
 
3.6%
ValueCountFrequency (%)
11 1
0.5%
51 1
0.5%
66 1
0.5%
121 1
0.5%
128 1
0.5%
143 2
1.0%
147 1
0.5%
169 1
0.5%
180 1
0.5%
202 1
0.5%
ValueCountFrequency (%)
9893038 1
0.5%
5006302 1
0.5%
2407672 1
0.5%
1732027 1
0.5%
1135886 1
0.5%
727973 1
0.5%
661710 1
0.5%
620302 1
0.5%
563449 1
0.5%
563325 1
0.5%

CPI
Real number (ℝ)

MISSING 

Distinct175
Distinct (%)98.3%
Missing17
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean190.46096
Minimum99.03
Maximum4583.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:22.970128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum99.03
5-th percentile104.8505
Q1113.885
median125.34
Q3157.265
95-th percentile267.6375
Maximum4583.71
Range4484.68
Interquartile range (IQR)43.38

Descriptive statistics

Standard deviation397.94738
Coefficient of variation (CV)2.0893909
Kurtosis94.031815
Mean190.46096
Median Absolute Deviation (MAD)15.565
Skewness9.3317385
Sum33902.05
Variance158362.12
MonotonicityNot monotonic
2023-11-17T10:44:23.199136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.62 2
 
1.0%
99.55 2
 
1.0%
106.58 2
 
1.0%
149.9 1
 
0.5%
114.24 1
 
0.5%
162.74 1
 
0.5%
109.32 1
 
0.5%
267.51 1
 
0.5%
120.27 1
 
0.5%
113.53 1
 
0.5%
Other values (165) 165
84.6%
(Missing) 17
 
8.7%
ValueCountFrequency (%)
99.03 1
0.5%
99.55 2
1.0%
99.7 1
0.5%
101.87 1
0.5%
102.51 1
0.5%
103.62 1
0.5%
103.87 1
0.5%
104.57 1
0.5%
104.9 1
0.5%
105.48 1
0.5%
ValueCountFrequency (%)
4583.71 1
0.5%
2740.27 1
0.5%
1344.19 1
0.5%
550.93 1
0.5%
418.34 1
0.5%
294.66 1
0.5%
288.57 1
0.5%
281.66 1
0.5%
268.36 1
0.5%
267.51 1
0.5%

GDP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)100.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.772959 × 1011
Minimum47271463
Maximum2.14277 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:23.463130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47271463
5-th percentile8.4054708 × 108
Q18.4546196 × 109
median3.4387229 × 1010
Q32.3409404 × 1011
95-th percentile1.7777586 × 1012
Maximum2.14277 × 1013
Range2.1427653 × 1013
Interquartile range (IQR)2.2563942 × 1011

Descriptive statistics

Standard deviation2.1721734 × 1012
Coefficient of variation (CV)4.5509994
Kurtosis78.039315
Mean4.772959 × 1011
Median Absolute Deviation (MAD)3.2413571 × 1010
Skewness8.578927
Sum9.2118109 × 1013
Variance4.7183371 × 1024
MonotonicityNot monotonic
2023-11-17T10:44:23.711657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2122450630 1
 
0.5%
2.069287655 × 10111
 
0.5%
1.252091529 × 10101
 
0.5%
1.292814512 × 10101
 
0.5%
4.481204289 × 10111
 
0.5%
3.21 × 10101
 
0.5%
1.022078107 × 10101
 
0.5%
4.033363636 × 10111
 
0.5%
7.698309493 × 10101
 
0.5%
3.044 × 10111
 
0.5%
Other values (183) 183
93.8%
(Missing) 2
 
1.0%
ValueCountFrequency (%)
47271463 1
0.5%
133000000 1
0.5%
194647202 1
0.5%
221278000 1
0.5%
283994900 1
0.5%
401932279 1
0.5%
429016605 1
0.5%
450353314 1
0.5%
596033333 1
0.5%
825385185 1
0.5%
ValueCountFrequency (%)
2.14277 × 10131
0.5%
1.991 × 10131
0.5%
5.081769542 × 10121
0.5%
3.845630031 × 10121
0.5%
2.827113185 × 10121
0.5%
2.715518274 × 10121
0.5%
2.611 × 10121
0.5%
2.029 × 10121
0.5%
2.001244392 × 10121
0.5%
1.839758041 × 10121
0.5%

Population
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean39831238
Minimum836
Maximum1.4257758 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:24.000224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum836
5-th percentile75275.1
Q11962678
median8826588
Q328585490
95-th percentile1.2608841 × 108
Maximum1.4257758 × 109
Range1.425775 × 109
Interquartile range (IQR)26622812

Descriptive statistics

Standard deviation1.4927723 × 108
Coefficient of variation (CV)3.7477426
Kurtosis76.511365
Mean39831238
Median Absolute Deviation (MAD)8244381.5
Skewness8.4594744
Sum7.7272603 × 109
Variance2.2283691 × 1016
MonotonicityNot monotonic
2023-11-17T10:44:24.252205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38041754 1
 
0.5%
182790 1
 
0.5%
6545502 1
 
0.5%
23310715 1
 
0.5%
200963599 1
 
0.5%
25666161 1
 
0.5%
1836713 1
 
0.5%
5347896 1
 
0.5%
5266535 1
 
0.5%
216565318 1
 
0.5%
Other values (184) 184
94.4%
ValueCountFrequency (%)
836 1
0.5%
10084 1
0.5%
11646 1
0.5%
18233 1
0.5%
33860 1
0.5%
38019 1
0.5%
38964 1
0.5%
52823 1
0.5%
58791 1
0.5%
71808 1
0.5%
ValueCountFrequency (%)
1425775850 1
0.5%
1425671352 1
0.5%
328239523 1
0.5%
270203917 1
0.5%
216565318 1
0.5%
212559417 1
0.5%
200963599 1
0.5%
167310838 1
0.5%
144373535 1
0.5%
126226568 1
0.5%

Infant mortality
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct144
Distinct (%)76.2%
Missing6
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean21.332804
Minimum1.4
Maximum84.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:24.533205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.24
Q16
median14
Q332.7
95-th percentile62.36
Maximum84.5
Range83.1
Interquartile range (IQR)26.7

Descriptive statistics

Standard deviation19.548058
Coefficient of variation (CV)0.91633795
Kurtosis0.57417273
Mean21.332804
Median Absolute Deviation (MAD)10.4
Skewness1.1585542
Sum4031.9
Variance382.12658
MonotonicityNot monotonic
2023-11-17T10:44:24.770211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 4
 
2.1%
3.1 4
 
2.1%
6.1 4
 
2.1%
12.4 3
 
1.5%
3.3 3
 
1.5%
2.6 3
 
1.5%
6.4 3
 
1.5%
9.8 3
 
1.5%
13.6 3
 
1.5%
2.7 3
 
1.5%
Other values (134) 156
80.0%
(Missing) 6
 
3.1%
ValueCountFrequency (%)
1.4 1
 
0.5%
1.5 1
 
0.5%
1.7 2
1.0%
1.8 1
 
0.5%
1.9 2
1.0%
2.1 2
1.0%
2.2 1
 
0.5%
2.3 2
1.0%
2.5 1
 
0.5%
2.6 3
1.5%
ValueCountFrequency (%)
84.5 1
0.5%
78.5 1
0.5%
76.6 1
0.5%
75.7 1
0.5%
71.4 1
0.5%
68.2 1
0.5%
65.7 1
0.5%
64.9 1
0.5%
63.7 1
0.5%
62.6 1
0.5%

Minimum wage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)76.0%
Missing45
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean2.1941333
Minimum0.01
Maximum13.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:25.016201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1435
Q10.4025
median1.045
Q32.4475
95-th percentile10.218
Maximum13.59
Range13.58
Interquartile range (IQR)2.045

Descriptive statistics

Standard deviation2.9707959
Coefficient of variation (CV)1.3539724
Kurtosis4.2430329
Mean2.1941333
Median Absolute Deviation (MAD)0.7
Skewness2.2020868
Sum329.12
Variance8.8256284
MonotonicityNot monotonic
2023-11-17T10:44:25.305202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
1.5%
0.41 3
 
1.5%
0.34 2
 
1.0%
2.25 2
 
1.0%
0.53 2
 
1.0%
0.35 2
 
1.0%
1.12 2
 
1.0%
0.6 2
 
1.0%
0.25 2
 
1.0%
1.23 2
 
1.0%
Other values (104) 128
65.6%
(Missing) 45
 
23.1%
ValueCountFrequency (%)
0.01 2
1.0%
0.05 2
1.0%
0.09 2
1.0%
0.12 1
0.5%
0.13 1
0.5%
0.16 1
0.5%
0.17 1
0.5%
0.18 1
0.5%
0.21 1
0.5%
0.23 2
1.0%
ValueCountFrequency (%)
13.59 1
0.5%
13.05 1
0.5%
11.72 1
0.5%
11.49 1
0.5%
11.16 1
0.5%
10.79 1
0.5%
10.31 1
0.5%
10.29 1
0.5%
10.13 1
0.5%
9.99 1
0.5%

Unemployment rate
Real number (ℝ)

MISSING 

Distinct164
Distinct (%)93.2%
Missing19
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean0.068863636
Minimum0.0009
Maximum0.2818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:25.588768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0009
5-th percentile0.013375
Q10.03395
median0.0536
Q30.0949
95-th percentile0.174775
Maximum0.2818
Range0.2809
Interquartile range (IQR)0.06095

Descriptive statistics

Standard deviation0.050792164
Coefficient of variation (CV)0.73757598
Kurtosis1.8803977
Mean0.068863636
Median Absolute Deviation (MAD)0.02655
Skewness1.3569113
Sum12.12
Variance0.0025798439
MonotonicityNot monotonic
2023-11-17T10:44:25.827757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0459 3
 
1.5%
0.0633 2
 
1.0%
0.1185 2
 
1.0%
0.0434 2
 
1.0%
0.0246 2
 
1.0%
0.0556 2
 
1.0%
0.0536 2
 
1.0%
0.0332 2
 
1.0%
0.0347 2
 
1.0%
0.0411 2
 
1.0%
Other values (154) 155
79.5%
(Missing) 19
 
9.7%
ValueCountFrequency (%)
0.0009 1
0.5%
0.0047 1
0.5%
0.0058 1
0.5%
0.0063 1
0.5%
0.0068 1
0.5%
0.0071 1
0.5%
0.0075 1
0.5%
0.0103 1
0.5%
0.0112 1
0.5%
0.0141 1
0.5%
ValueCountFrequency (%)
0.2818 1
0.5%
0.2341 1
0.5%
0.2071 1
0.5%
0.2027 1
0.5%
0.2 1
0.5%
0.1888 1
0.5%
0.1856 1
0.5%
0.1842 1
0.5%
0.1819 1
0.5%
0.1724 1
0.5%
Distinct145
Distinct (%)82.4%
Missing19
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean0.62738068
Minimum0.38
Maximum0.868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:26.096763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile0.4455
Q10.5615
median0.6245
Q30.695
95-th percentile0.80825
Maximum0.868
Range0.488
Interquartile range (IQR)0.1335

Descriptive statistics

Standard deviation0.10502907
Coefficient of variation (CV)0.16740884
Kurtosis-0.30696739
Mean0.62738068
Median Absolute Deviation (MAD)0.065
Skewness-0.015166251
Sum110.419
Variance0.011031106
MonotonicityNot monotonic
2023-11-17T10:44:26.390832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.688 3
 
1.5%
0.651 3
 
1.5%
0.72 3
 
1.5%
0.68 2
 
1.0%
0.838 2
 
1.0%
0.64 2
 
1.0%
0.699 2
 
1.0%
0.724 2
 
1.0%
0.565 2
 
1.0%
0.664 2
 
1.0%
Other values (135) 153
78.5%
(Missing) 19
 
9.7%
ValueCountFrequency (%)
0.38 1
0.5%
0.393 1
0.5%
0.412 1
0.5%
0.42 1
0.5%
0.43 1
0.5%
0.431 1
0.5%
0.433 1
0.5%
0.437 1
0.5%
0.441 1
0.5%
0.447 1
0.5%
ValueCountFrequency (%)
0.868 1
0.5%
0.861 1
0.5%
0.838 2
1.0%
0.837 1
0.5%
0.834 1
0.5%
0.831 1
0.5%
0.823 1
0.5%
0.821 1
0.5%
0.804 1
0.5%
0.796 1
0.5%

temperature change per country in 2022
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing195
Missing (%)100.0%
Memory size1.6 KiB

temperature
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct143
Distinct (%)97.9%
Missing49
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean19.893425
Minimum-4.21
Maximum32.15
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)1.5%
Memory size1.6 KiB
2023-11-17T10:44:26.632833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.21
5-th percentile3.285
Q112.405
median23.85
Q326.735
95-th percentile29.7775
Maximum32.15
Range36.36
Interquartile range (IQR)14.33

Descriptive statistics

Standard deviation8.7701592
Coefficient of variation (CV)0.44085719
Kurtosis-0.50277294
Mean19.893425
Median Absolute Deviation (MAD)4.72
Skewness-0.72390016
Sum2904.44
Variance76.915693
MonotonicityNot monotonic
2023-11-17T10:44:26.887836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.42 2
 
1.0%
24.66 2
 
1.0%
11.38 2
 
1.0%
23.12 1
 
0.5%
27.61 1
 
0.5%
24.53 1
 
0.5%
27.53 1
 
0.5%
27.01 1
 
0.5%
25.35 1
 
0.5%
23.46 1
 
0.5%
Other values (133) 133
68.2%
(Missing) 49
 
25.1%
ValueCountFrequency (%)
-4.21 1
0.5%
-3.57 1
0.5%
-0.29 1
0.5%
0.15 1
0.5%
0.93 1
0.5%
2.11 1
0.5%
2.7 1
0.5%
3.04 1
0.5%
4.02 1
0.5%
5.33 1
0.5%
ValueCountFrequency (%)
32.15 1
0.5%
31.42 1
0.5%
31.36 1
0.5%
31.31 1
0.5%
30.98 1
0.5%
30.92 1
0.5%
30.78 1
0.5%
29.8 1
0.5%
29.71 1
0.5%
29.48 1
0.5%

ideal temperature?
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
117 
1
78 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Length

2023-11-17T10:44:27.144365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T10:44:27.380887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Most occurring characters

ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common 195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
60.0%
1 78
40.0%

Precipitation Depth (mm/year)
Real number (ℝ)

MISSING 

Distinct172
Distinct (%)95.6%
Missing15
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1170.0228
Minimum18.1
Maximum3240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:27.584450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile120.5
Q1564.25
median1031
Q31706.75
95-th percentile2703
Maximum3240
Range3221.9
Interquartile range (IQR)1142.5

Descriptive statistics

Standard deviation800.55786
Coefficient of variation (CV)0.68422416
Kurtosis-0.41209861
Mean1170.0228
Median Absolute Deviation (MAD)536.5
Skewness0.64711645
Sum210604.1
Variance640892.88
MonotonicityNot monotonic
2023-11-17T10:44:27.864959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1274 2
 
1.0%
1500 2
 
1.0%
228 2
 
1.0%
250 2
 
1.0%
2200 2
 
1.0%
788 2
 
1.0%
900 2
 
1.0%
282 2
 
1.0%
1414 1
 
0.5%
2280 1
 
0.5%
Other values (162) 162
83.1%
(Missing) 15
 
7.7%
ValueCountFrequency (%)
18.1 1
0.5%
56 1
0.5%
59 1
0.5%
74 1
0.5%
78 1
0.5%
83 1
0.5%
89 1
0.5%
92 1
0.5%
111 1
0.5%
121 1
0.5%
ValueCountFrequency (%)
3240 1
0.5%
3200 1
0.5%
3142 1
0.5%
3028 1
0.5%
2928 1
0.5%
2926 1
0.5%
2880 1
0.5%
2875 1
0.5%
2722 1
0.5%
2702 1
0.5%

Gini's index
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)76.0%
Missing45
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean36.995333
Minimum23.2
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:28.462223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile26.045
Q131.7
median36
Q341.175
95-th percentile51.255
Maximum63
Range39.8
Interquartile range (IQR)9.475

Descriptive statistics

Standard deviation7.6583975
Coefficient of variation (CV)0.20700982
Kurtosis0.43885035
Mean36.995333
Median Absolute Deviation (MAD)4.9
Skewness0.71391747
Sum5549.3
Variance58.651052
MonotonicityNot monotonic
2023-11-17T10:44:28.712765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.7 4
 
2.1%
38.8 3
 
1.5%
36.8 3
 
1.5%
36 3
 
1.5%
26 3
 
1.5%
31.7 3
 
1.5%
34.2 3
 
1.5%
29.6 2
 
1.0%
37.1 2
 
1.0%
29.5 2
 
1.0%
Other values (104) 122
62.6%
(Missing) 45
 
23.1%
ValueCountFrequency (%)
23.2 1
 
0.5%
24 1
 
0.5%
24.4 1
 
0.5%
25.6 1
 
0.5%
25.7 1
 
0.5%
26 3
1.5%
26.1 1
 
0.5%
26.2 1
 
0.5%
27.1 1
 
0.5%
27.5 1
 
0.5%
ValueCountFrequency (%)
63 1
0.5%
59.1 1
0.5%
55.9 1
0.5%
54.6 1
0.5%
53.3 1
0.5%
52.9 1
0.5%
51.5 1
0.5%
51.3 1
0.5%
51.2 1
0.5%
50.9 1
0.5%

GDP per capita
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15608.147
Minimum0
Maximum184396.99
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:28.996765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile503.64107
Q11851.6887
median5768.6864
Q317370.407
95-th percentile61557.967
Maximum184396.99
Range184396.99
Interquartile range (IQR)15518.719

Descriptive statistics

Standard deviation25228.362
Coefficient of variation (CV)1.6163586
Kurtosis17.943207
Mean15608.147
Median Absolute Deviation (MAD)4775.156
Skewness3.6219339
Sum3027980.4
Variance6.3647027 × 108
MonotonicityNot monotonic
2023-11-17T10:44:29.262295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502.1154869 1
 
0.5%
11611.41545 1
 
0.5%
1912.903745 1
 
0.5%
554.6009687 1
 
0.5%
2229.858696 1
 
0.5%
1250.673991 1
 
0.5%
5564.713196 1
 
0.5%
75419.63487 1
 
0.5%
14617.40878 1
 
0.5%
1405.580556 1
 
0.5%
Other values (184) 184
94.4%
ValueCountFrequency (%)
0 1
0.5%
261.2474725 1
0.5%
305.6890707 1
0.5%
326.0630992 1
0.5%
411.5523404 1
0.5%
441.5056034 1
0.5%
467.9074407 1
0.5%
478.1543714 1
0.5%
491.8047231 1
0.5%
502.1154869 1
0.5%
ValueCountFrequency (%)
184396.9868 1
0.5%
172357.4723 1
0.5%
110172.3731 1
0.5%
81993.72715 1
0.5%
77629.98899 1
0.5%
75419.63487 1
0.5%
66944.82551 1
0.5%
65280.68224 1
0.5%
65233.28244 1
0.5%
64781.7332 1
0.5%

Human Development Index (2021)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct160
Distinct (%)84.2%
Missing5
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.71887368
Minimum0.385
Maximum0.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:29.506428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.4704
Q10.59825
median0.735
Q30.83175
95-th percentile0.93955
Maximum0.962
Range0.577
Interquartile range (IQR)0.2335

Descriptive statistics

Standard deviation0.15074097
Coefficient of variation (CV)0.20969049
Kurtosis-0.87611491
Mean0.71887368
Median Absolute Deviation (MAD)0.119
Skewness-0.28399476
Sum136.586
Variance0.022722841
MonotonicityNot monotonic
2023-11-17T10:44:29.738451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.745 4
 
2.1%
0.767 3
 
1.5%
0.875 3
 
1.5%
0.802 3
 
1.5%
0.607 3
 
1.5%
0.593 2
 
1.0%
0.535 2
 
1.0%
0.855 2
 
1.0%
0.558 2
 
1.0%
0.809 2
 
1.0%
Other values (150) 164
84.1%
(Missing) 5
 
2.6%
ValueCountFrequency (%)
0.385 1
0.5%
0.394 1
0.5%
0.4 1
0.5%
0.404 1
0.5%
0.426 1
0.5%
0.428 1
0.5%
0.446 1
0.5%
0.449 1
0.5%
0.455 1
0.5%
0.465 1
0.5%
ValueCountFrequency (%)
0.962 1
0.5%
0.961 1
0.5%
0.959 1
0.5%
0.951 1
0.5%
0.948 1
0.5%
0.947 1
0.5%
0.945 1
0.5%
0.942 1
0.5%
0.941 1
0.5%
0.94 1
0.5%
Distinct128
Distinct (%)87.1%
Missing48
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean35.729932
Minimum2.7
Maximum92.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-11-17T10:44:30.034424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile5.86
Q111.5
median31.1
Q358.55
95-th percentile83.44
Maximum92.2
Range89.5
Interquartile range (IQR)47.05

Descriptive statistics

Standard deviation26.384991
Coefficient of variation (CV)0.73845622
Kurtosis-0.99442281
Mean35.729932
Median Absolute Deviation (MAD)21.5
Skewness0.55223192
Sum5252.3
Variance696.16773
MonotonicityNot monotonic
2023-11-17T10:44:30.261426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9 3
 
1.5%
6 2
 
1.0%
9.6 2
 
1.0%
15.4 2
 
1.0%
76.6 2
 
1.0%
37.1 2
 
1.0%
6.5 2
 
1.0%
11.5 2
 
1.0%
10 2
 
1.0%
25.9 2
 
1.0%
Other values (118) 126
64.6%
(Missing) 48
 
24.6%
ValueCountFrequency (%)
2.7 1
0.5%
3.2 1
0.5%
3.6 1
0.5%
5 1
0.5%
5.3 2
1.0%
5.7 1
0.5%
5.8 1
0.5%
6 2
1.0%
6.5 2
1.0%
6.8 1
0.5%
ValueCountFrequency (%)
92.2 1
0.5%
90.3 1
0.5%
88.6 1
0.5%
87.4 1
0.5%
84.7 1
0.5%
84.6 1
0.5%
84.3 1
0.5%
83.8 1
0.5%
82.6 1
0.5%
82 1
0.5%

Interactions

2023-11-17T10:44:12.903033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:30.275283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:33.146309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:36.015706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.898940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:41.646398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:44.934442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:47.628673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:50.736393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.346363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:56.292705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:59.596701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:02.870957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:06.500680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:09.557496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:13.114091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:30.484285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:33.348394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:36.182704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.061940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:41.832413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.095437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:47.814633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:50.947388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.450363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:56.514238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:59.797232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:03.139491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:06.713686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:09.748071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:13.351088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:30.658283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:33.528944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:36.659706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.245485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:42.021976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.303433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.025645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:51.140393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.626370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:56.712250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:00.032233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:03.393029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:06.922683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:09.973055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:13.604635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:30.828285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:33.698942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:36.820230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.432480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:42.228942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.453459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.195634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:51.322388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.823367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:56.926773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:00.232774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:03.605032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:07.094687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:10.147055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:13.830629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.005284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:33.871947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:36.990231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.593486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:42.427294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.617756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.360706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:51.490389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:54.020911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:57.508574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:00.434796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:03.851032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:07.316229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:10.355056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:14.058631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.197289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:34.085482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:37.174760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.788475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:42.622300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.821274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.565683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:51.702910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:54.225914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:57.702571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:00.639318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:04.081051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:07.549369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:10.556059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:14.255176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.372289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:34.295074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:37.333857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:39.945061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:42.804305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:45.994271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.729241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:51.880910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:54.423917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:57.899104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:00.826301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:04.286030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:07.758370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:10.745054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:14.468176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.574282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:34.514614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:37.513838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:40.140260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:43.006395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:46.180268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:48.913313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:52.077938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:54.696525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:58.105097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:01.067481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:04.517030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:07.953370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:10.948059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:14.695180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.777818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:34.686165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:37.682838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:40.314790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:43.527375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:46.347268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:49.081311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:52.311213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:54.903059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:58.293101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:01.262484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:05.096179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:08.136395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:11.150060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:14.917254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:31.992821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:34.885164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:37.851869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:40.515789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:43.721374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:46.530177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:49.247313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:52.488763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:55.099058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:58.470099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:01.457484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:05.291162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:08.322394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:11.356065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:15.137253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:32.207818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:35.073164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.034839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:40.702340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:43.946376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:46.714179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:49.435310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:52.688287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:55.298056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:58.669697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:01.678033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:05.506156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:08.518368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:11.540603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:15.346253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:32.394199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:35.258172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.202839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:40.879341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:44.155901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:46.888202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:49.643849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:52.898821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:55.489057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:58.850698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:01.936111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:05.718158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:08.715115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:11.746622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:15.554788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:32.588775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:35.455163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.398940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:41.062870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:44.340442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:47.081202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:49.814870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.031820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:55.709158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:59.043698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:02.181638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:05.915165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:08.911951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:11.939600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:15.748812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:32.769776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:35.653162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.558940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:41.272865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:44.559441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:47.264791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:50.343849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.132816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:55.890159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:59.222699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:02.429811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:06.102683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:09.130949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:12.513416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:15.940789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:32.950787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:35.832174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:38.728942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:41.466397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:44.751443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:47.450110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:50.555849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:53.249815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:56.072703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:43:59.406698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:02.653361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:06.292686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:09.346946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T10:44:12.715419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-17T10:44:30.485967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Agricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperaturePrecipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)ideal temperature?
Agricultural Land( %)1.0000.0740.2320.0780.2770.200-0.1910.056-0.151-0.081-0.2490.112-0.287-0.2140.1460.000
Co2-Emissions per ton0.0741.0000.0610.9480.728-0.4480.307-0.050-0.190-0.258-0.312-0.1740.4030.475-0.5320.037
CPI0.2320.0611.0000.0080.3380.434-0.4890.0570.0320.1420.0460.249-0.483-0.4210.3900.000
GDP0.0780.9480.0081.0000.747-0.4610.334-0.110-0.135-0.250-0.213-0.1590.4300.510-0.5570.000
Population0.2770.7280.3380.7471.0000.120-0.157-0.141-0.0320.098-0.1980.130-0.201-0.105-0.0090.114
Infant mortality0.200-0.4480.434-0.4610.1201.000-0.728-0.0200.1690.5940.0960.445-0.873-0.9320.8480.315
Minimum wage-0.1910.307-0.4890.334-0.157-0.7281.0000.108-0.235-0.397-0.074-0.2820.8240.774-0.6250.318
Unemployment rate0.056-0.0500.057-0.110-0.141-0.0200.1081.000-0.494-0.201-0.2130.1210.0550.0410.0540.131
Population: Labor force participation (%)-0.151-0.1900.032-0.135-0.0320.169-0.235-0.4941.0000.2580.2390.326-0.136-0.1790.1870.225
temperature-0.081-0.2580.142-0.2500.0980.594-0.397-0.2010.2581.0000.2360.481-0.462-0.5800.6460.880
Precipitation Depth (mm/year)-0.249-0.3120.046-0.213-0.1980.096-0.074-0.2130.2390.2361.0000.233-0.035-0.1030.1060.380
Gini's index0.112-0.1740.249-0.1590.1300.445-0.2820.1210.3260.4810.2331.000-0.368-0.4620.5900.345
GDP per capita-0.2870.403-0.4830.430-0.201-0.8730.8240.055-0.136-0.462-0.035-0.3681.0000.940-0.8060.188
Human Development Index (2021)-0.2140.475-0.4210.510-0.105-0.9320.7740.041-0.179-0.580-0.103-0.4620.9401.000-0.8670.339
Prevalence of moderate or severe food insecurity in the total population (percent) (2022)0.146-0.5320.390-0.557-0.0090.848-0.6250.0540.1870.6460.1060.590-0.806-0.8671.0000.288
ideal temperature?0.0000.0370.0000.0000.1140.3150.3180.1310.2250.8800.3800.3450.1880.3390.2881.000

Missing values

2023-11-17T10:44:16.245812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T10:44:16.719792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-17T10:44:17.311394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryCountry CodeAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperature change per country in 2022temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)
0AfghanistanAFG0.5818672.0149.901.910135e+1038041754.047.90.430.11120.489NaNNaN0327.0NaN502.1154870.47882.6
1AlbaniaALB0.4314536.0119.051.527808e+102854191.07.81.120.12330.557NaN15.9701485.029.45352.8574110.79633.1
2AlgeriaNaN0.174150006.0151.361.699882e+1143053054.020.10.950.11700.412NaN26.30189.027.63948.3432790.74522.6
3AndorraNaN0.400469.0NaN3.154058e+0977142.02.76.63NaNNaNNaNNaN0NaNNaN40886.3911620.858NaN
4AngolaNaN0.47534693.0261.739.463542e+1031825295.051.60.710.06890.775NaN26.6911010.051.32973.5911600.58679.9
5Antigua and BarbudaNaN0.205557.0113.811.727759e+0997118.05.03.04NaNNaNNaNNaN01030.0NaN17790.3093040.78836.5
6ArgentinaNaN0.543201348.0232.754.496634e+1144938712.08.83.350.09790.613NaN16.750591.042.010006.1489740.84240.5
7ArmeniaNaN0.5895156.0129.181.367280e+102957731.011.00.660.16990.556NaN7.560562.027.94622.7334930.7598.3
8AustraliaNaN0.482375908.0119.801.392681e+1225766605.03.113.590.05270.655NaN24.351534.034.354049.8288120.95113.2
9AustriaNaN0.32461448.0118.064.463147e+118877067.02.9NaN0.04670.607NaN9.0401110.029.850277.2750870.9165.3
CountryCountry CodeAgricultural Land( %)Co2-Emissions per tonCPIGDPPopulationInfant mortalityMinimum wageUnemployment ratePopulation: Labor force participation (%)temperature change per country in 2022temperatureideal temperature?Precipitation Depth (mm/year)Gini's indexGDP per capitaHuman Development Index (2021)Prevalence of moderate or severe food insecurity in the total population (percent) (2022)
185United KingdomNaN0.717379025.0119.622.827113e+1266834405.03.610.130.03850.628NaN10.4301220.032.642300.2671260.9295.0
186United StatesNaN0.4445006302.0117.242.142770e+13328239523.05.67.250.14700.620NaN8.040715.039.865280.6822410.9217.9
187UruguayNaN0.8266766.0202.925.604591e+103461734.06.41.660.08730.640NaN20.3911300.040.816190.1269570.80916.3
188UzbekistanNaN0.62991811.0NaN5.792129e+1033580650.019.10.240.05920.651NaN14.300206.0NaN1724.8411340.72728.7
189VanuatuNaN0.153147.0117.139.170589e+08299882.022.31.560.04390.699NaNNaN02000.032.33058.0656760.60725.5
190VenezuelaNaN0.245164175.02740.274.823593e+1128515829.021.40.010.08800.597NaNNaN02044.0NaN16915.4934530.691NaN
191VietnamNaN0.393192668.0163.522.619212e+1196462106.016.50.730.02010.774NaN24.6611821.036.82715.2760360.70310.0
192YemenNaN0.44610609.0157.582.691440e+1029161922.042.9NaN0.12910.380NaNNaN0167.036.7922.9296420.45571.2
193ZambiaNaN0.3215141.0212.312.306472e+1017861030.040.40.240.11430.746NaN26.7511020.055.91291.3433570.56576.0
194ZimbabweNaN0.41910983.0105.512.144076e+1014645468.033.9NaN0.04950.831NaN25.371657.050.31463.9859100.59377.8